Bei der kurzfristigen Einsatzplanung von Energiesystemen liegt der Schwerpunkt zunehmend auf der Identifikation, Bewertung und Überwindung von Schwachstellen in Stromnetzen. Dieser wichtige Leitfaden untersucht moderne Methodiken zur Bewertung und Verbesserung der Sicherheit von Energiesystemen bei der kurzfristigen Einsatzplanung und im Echtbetrieb. Die Methodiken nutzen fortschrittliche Methoden aus der Wahrscheinlichkeitstheorie, aus den Bereichen Data Mining, künstliche Intelligenz und Optimierung, um Überwachungs-, (vorbeugende und korrigierende) Steuerungsaufgaben sowie Entscheidungen wissensbasiert durchführen und treffen zu können.Hauptmerkmale: Beschreibt, wie sich Netze durch Überwachung des Stromflusses intelligent steuern, schützen und optimal verwalten lassen. Vermittelt alles Wissenswerte rund um risikobasierte Zuverlässigkeits- und Sicherheitsbewertungen, dynamische Schwachstellenbewertungsmethoden. Zurückgegriffen wird dabei auf die Mathematik. Vermittelt das Expertenwissen zu Mitigationsmaßnahmen, die intelligente Schutz- und Steuerungsverfahren, kontrollierte Inselbildung, modellprädikative Regelung, Multi-Agenten-Systeme und verteilte Steuerungssysteme nutzen. Zeig die Implementierung in intelligente Netze und Anwendungen zur Eigenreparatur anhand von Beispielen und Erfahrungen aus der Praxis und mittels des WAMPAC-Schemas. Begleitende Website mit Zusatzmaterialien, darunter Matlab-Codes.

List of Contributors xvForeword xixPreface xxi1 Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment 1Jaime C. Cepeda and José Luis Rueda-Torres1.1 Introduction 11.2 Power System Vulnerability 21.2.1 Vulnerability Assessment 21.2.2 Timescale of Power System Actions and Operations 41.3 Power System Vulnerability Symptoms 51.3.1 Rotor Angle Stability 61.3.2 Short-Term Voltage Stability 71.3.3 Short-Term Frequency Stability 71.3.4 Post-Contingency Overloads 71.4 Synchronized Phasor Measurement Technology 81.4.1 Phasor Representation of Sinusoids 81.4.2 Synchronized Phasors 91.4.3 Phasor Measurement Units (PMUs) 91.4.4 Discrete Fourier Transform and Phasor Calculation 101.4.5 Wide Area Monitoring Systems 101.4.6 WAMPAC Communication Time Delay 121.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment 131.6 Concluding Remarks 162 Steady-state Security 21Evelyn Heylen, Steven De Boeck, Marten Ovaere, Hakan Ergun, and Dirk Van Hertem2.1 Power System Reliability Management: A Combination of Reliability Assessment and Reliability Control 222.1.1 Reliability Assessment 232.1.2 Reliability Control 242.2 Reliability Under Various Timeframes 312.3 Reliability Criteria 332.4 Reliability and Its Cost as a Function of Uncertainty 342.4.1 Reliability Costs 342.4.2 Interruption Costs 352.4.3 Minimizing the Sum of Reliability and Interruption Costs 363 Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems 41Bart W. Tuinema, Nikoleta Kandalepa, and José Luis Rueda-Torres3.1 Introduction 413.2 Time Horizons in the Planning and Operation of Power Systems 423.2.1 Time Horizons 423.2.2 Overlapping and Interaction 423.2.3 Remedial Actions 423.3 Reliability Indicators 453.3.1 Security-of-Supply Related Indicators 453.3.2 Additional Indicators 473.4 Reliability Analysis 493.4.1 Input Information 493.4.2 Pre-calculations 503.4.3 Reliability Analysis 503.4.4 Output: Reliability Indicators 533.5 Application Example: EHV Underground Cables 533.5.1 Input Parameters 543.5.2 Results of Analysis 564 An Enhanced WAMS-based Power System Oscillation Analysis Approach 63Qing Liu, Hassan Bevrani, and Yasunori Mitani4.1 Introduction 634.2 HHT Method 654.2.1 EMD 654.2.2 Hilbert Transform 654.2.3 Hilbert Spectrum and Hilbert Marginal Spectrum 664.2.4 HHT Issues 674.3 The Enhanced HHT Method 714.3.1 Data Pre-treatment Processing 714.3.2 Inhibiting the Boundary End Effect 754.3.3 Parameter Identification 804.4 Enhanced HHT Method Evaluation 814.4.1 Case I 814.4.2 Case II 844.4.3 Case III 854.5 Application to RealWide Area Measurements 885 Pattern Recognition-Based Approach for Dynamic Vulnerability Status Prediction 95Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich5.1 Introduction 955.2 Post-contingency Dynamic Vulnerability Regions 965.3 Recognition of Post-contingency DVRs 975.3.1 N-1 Contingency Monte Carlo Simulation 985.3.2 Post-contingency Pattern Recognition Method 1005.3.3 Definition of Data-TimeWindows 1035.3.4 Identification of Post-contingency DVRs--Case Study 1045.4 Real-Time Vulnerability Status Prediction 1095.4.1 Support Vector Classifier (SVC) Training 1125.4.2 SVC Real-Time Implementation 1135.5 Concluding Remarks 1156 Performance Indicator-Based Real-Time Vulnerability Assessment 119Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich6.1 Introduction 1196.2 Overview of the Proposed Vulnerability Assessment Methodology 1206.3 Real-Time Area Coherency Identification 1226.3.1 Associated PMU Coherent Areas 1226.4 TVFS Vulnerability Performance Indicators 1256.4.1 Transient Stability Index (TSI) 1256.4.2 Voltage Deviation Index (VDI) 1286.4.3 Frequency Deviation Index (FDI) 1316.4.4 Assessment of TVFS Security Level for the Illustrative Examples 1316.4.5 Complete TVFS Real-Time Vulnerability Assessment 1336.5 Slower Phenomena Vulnerability Performance Indicators 1376.5.1 Oscillatory Index (OSI) 1376.5.2 Overload Index (OVI) 1416.6 Concluding Remarks 1457 Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems 149Florin Capitanescu7.1 Chapter Overview 1497.2 Conventional (Deterministic) AC Optimal Power Flow (OPF) 1507.2.1 Introduction 1507.2.2 Abstract Mathematical Formulation of the OPF Problem 1507.2.3 OPF Solution via Interior-Point Method 1517.2.4 Illustrative Example 1547.3 Risk-Based OPF 1587.3.1 Motivation and Principle 1587.3.2 Risk-Based OPF Problem Formulation 1597.3.3 Illustrative Example 1607.4 OPF Under Uncertainty 1627.4.1 Motivation and Potential Approaches 1627.4.2 Robust Optimization Framework 1627.4.3 Methodology for Solving the R-OPF Problem 1637.4.4 Illustrative Example 1647.5 Advanced Issues and Outlook 1697.5.1 Conventional OPF 1697.5.2 Beyond the Scope of Conventional OPF: Risk, Uncertainty, Smarter Sustainable Grid 1728 Modeling Preventive and Corrective Actions Using Linear Formulation 177Tom Van Acker and Dirk Van Hertem8.1 Introduction 1778.2 Security Constrained OPF 1788.3 Available Control Actions in AC Power Systems 1788.3.1 Generator Redispatch 1798.3.2 Load Shedding and Demand Side Management 1798.3.3 Phase Shifting Transformer 1798.3.4 Switching Actions 1808.3.5 Reactive Power Management 1808.3.6 Special Protection Schemes 1808.4 Linear Implementation of Control Actions in a SCOPF Environment 1808.4.1 Generator Redispatch 1818.4.2 Load Shedding and Demand Side Management 1828.4.3 Phase Shifting Transformer 1838.4.4 Switching 1848.5 Case Study of Preventive and Corrective Actions 1858.5.1 Case Study 1: Generator Redispatch and Load Shedding (CS1) 1868.5.2 Case Study 2: Generator Redispatch, Load Shedding and PST (CS2) 1878.5.3 Case Study 3: Generator Redispatch, Load Shedding and Switching (CS3) 1909 Model-based Predictive Control for Damping Electromechanical Oscillations in Power Systems 193DaWang9.1 Introduction 1939.2 MPC BasicTheory & Damping Controller Models 1949.2.1 What is MPC? 1949.2.2 Damping Controller Models 1969.3 MPC for Damping Oscillations 1989.3.1 Outline of Idea 1989.3.2 Mathematical Formulation 1999.3.3 Proposed Control Schemes 2009.4 Test System & Simulation Setting 2049.5 Performance Analysis of MPC Schemes 2049.5.1 Centralized MPC 2049.5.2 Distributed MPC 2099.5.3 Hierarchical MPC 2099.6 Conclusions and Discussions 21310 Voltage Stability Enhancement by Computational Intelligence Methods 217Worawat Nakawiro10.1 Introduction 21710.2 Theoretical Background 21810.2.1 Voltage Stability Assessment 21810.2.2 Sensitivity Analysis 21910.2.3 Optimal Power Flow 22010.2.4 Artificial Neural Network 22010.2.5 Ant Colony Optimisation 22110.3 Test Power System 22310.4 Example 1: Preventive Measure 22410.4.1 Problem Statement 22410.4.2 Simulation Results 22510.5 Example 2: Corrective Measure 22610.5.1 Problem Statement 22610.5.2 Simulation Results 22711 Knowledge-Based Primary and Optimization-Based Secondary Control of Multi-terminal HVDCGrids 233Adedotun J. Agbemuko, Mario Ndreko, Marjan Popov, José Luis Rueda-Torres, and Mart A.M.M van der Meijden11.1 Introduction 23411.2 Conventional Control Schemes in HV-MTDC Grids 23411.3 Principles of Fuzzy-Based Control 23611.4 Implementation of the Knowledge-Based Power-Voltage Droop Control Strategy 23611.4.1 Control Scheme for Primary and Secondary Power-Voltage Control 23711.4.2 Input/Output Variables 23811.4.3 Knowledge Base and Inference Engine 24111.4.4 Defuzzification and Output 24111.5 Optimization-Based Secondary Control Strategy 24211.5.1 Fitness Function 24211.5.2 Constraints 24411.6 Simulation Results 24511.6.1 Set Point Change 24511.6.2 Constantly Changing Reference Set Points 24611.6.3 Sudden Disconnection ofWind Farm for Undefined Period 24611.6.4 Permanent Outage of VSC 3 24712 Model Based Voltage/Reactive Control in Sustainable Distribution Systems 251Hoan Van Pham and Sultan Nasiruddin Ahmed12.1 Introduction 25112.2 BackgroundTheory 25212.2.1 Voltage Control 25212.2.2 Model Predictive Control 25312.2.3 Model Analysis 25512.2.4 Implementation 25712.3 MPC Based Voltage/Reactive Controller - an Example 25812.3.1 Control Scheme 25812.3.2 Overall Objective Function of the MPC Based Controller 25912.3.3 Implementation of the MPC Based Controller 26112.4 Test Results 26212.4.1 Test System and Measurement Deployment 26212.4.2 Parameter Setup and Algorithm Selection for the Controller 26312.4.3 Results and Discussion 26312.5 Conclusions 26613 Multi-Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems 269Hoan Van Pham and Sultan Nasiruddin Ahmed13.1 Introduction 26913.2 System Model and Problem Formulation 27013.3 Multi-Agent Based Approach 27513.3.1 Augmented Lagrange Formulation 27513.3.2 Implementation Algorithm 27513.4 Case Studies and Simulation Results 27713.4.1 Case Studies 27713.4.2 Simulation Results 27714 Operation of Distribution SystemsWithin Secure Limits Using Real-Time Model Predictive Control 283Hamid Soleimani Bidgoli, Gustavo Valverde, Petros Aristidou, Mevludin Glavic, and Thierry Van Cutsem14.1 Introduction 28314.2 Basic MPC Principles 28514.3 Control Problem Formulation 28514.4 Voltage CorrectionWith Minimum Control Effort 28814.4.1 Inclusion of LTC Actions as Known Disturbances 28914.4.2 Problem Formulation 29014.5 Correction of Voltages and Congestion Management with Minimum Deviation from References 29114.5.4 Problem Formulation 29514.6 Test System 29614.7 Simulation Results: Voltage Correction with Minimal Control Effort 29814.8 Simulation Results: Voltage and/or Congestion Corrections with Minimum Deviation from Reference 30215 Enhancement of Transmission System Voltage Stability through Local Control of Distribution Networks 311Gustavo Valverde, Petros Aristidou, and Thierry Van Cutsem15.1 Introduction 31115.2 Long-Term Voltage Stability 31315.2.1 Countermeasures 31415.3 Impact of Volt-VAR Control on Long-Term Voltage Stability 31615.3.1 Countermeasures 31815.4 Test System Description 31915.4.1 Test System 31915.4.2 VVC Algorithm 32115.4.3 Emergency Detection 32215.5 Case Studies and Simulation Results 32315.5.1 Results in Stable Scenarios 32315.5.2 Results in Unstable Scenarios 32615.5.3 Results with Emergency Support From Distribution 32816 Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints 337Nelson Granda and Delia G. Colomé16.1 Introduction 33716.1.1 Stage One: Vulnerability Assessment 33716.1.2 Stage Two: Islanding Process 33816.2 Network Splitting Mechanism 34016.2.1 Graph Modeling, Update, and Reduction 34116.2.2 Graph Partitioning Procedure 34216.2.3 Load Shedding/Generation Tripping Schemes 34316.2.4 Tie-Lines Determination 34416.3 Power Imbalance Constraint Limits 34416.3.1 Reduced Frequency ResponseModel 34516.3.2 Power Imbalance Constraint Limits Determination 34716.4 Overload Assessment and Control 34816.5 Test Results 34916.5.1 Power System Collapse 34916.5.2 Application of Proposed Methodology 35116.5.3 Performance of Proposed ACIS 35416.6 Conclusions and Recommendations 35617 High-Speed Transmission Line Protection Based on Empirical Orthogonal Functions 361Rommel P. Aguilar and Fabián E. Pérez-Yauli17.1 Introduction 36117.2 Empirical Orthogonal Functions 36317.2.1 Formulation 36317.3 Applications of EOFs for Transmission Line Protection 36517.3.1 Fault Direction 36617.3.2 Fault Classification 36717.3.3 Fault Location 36917.4 Study Case 36917.4.1 Transmission Line Model and Simulation 36917.4.2 The Power System and Transmission Line 37017.4.3 Training Data 37017.4.4 Training Data Matrix 37017.4.5 Signal Conditioning 37317.4.6 Energy Patterns 37317.4.7 EOF Analysis 37617.4.8 Evaluation of the Protection Scheme 37917.4.9 Fault Classification 38017.4.10 Fault Location 38217.5 Conclusions 383Study Cases:WECC 9-bus, ATPDrawModels and Parameters 38418 Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System 389Pablo X. Verdugo, Jaime C. Cepeda, Aharon B. De La Torre, and Diego E. Echeverría18.1 Introduction 38918.2 PMU Location in the Ecuadorian SNI 39018.3 Steady-State Angle Stability 39118.4 Steady-State Voltage Stability 39518.5 Oscillatory Stability 39818.5.1 Power System Stabilizer Tuning 40218.6 Ecuadorian Special Protection Scheme (SPS) 40718.6.1 SPS Operation Analysis 40918.7 Concluding Remarks 410Index 413